# Acumatica Data Load Window Calculator

> Source: https://netray.co/tools/acumatica-data-load-window-calculator
> Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems.
> Section: Free Tools

## Short answer

Estimates cutover-weekend load time from record volumes, load throughput, error rework and parallel streams.

## Category

erp-migration

## Erp

Acumatica

## Industry

SMB Manufacturing

## Audience

- Data migration lead
- Project manager
- ERP consultant

## Tool

**Kind:** calculator

**Inputs:**

#### Master records (customers, vendors, items)

**Key:** master

**Type:** number

**Default:** 25000

**Min:** 0

**Step:** 500

**Unit:** records

#### Open transactions (orders, invoices, balances)

**Key:** txn

**Type:** number

**Default:** 12000

**Min:** 0

**Step:** 500

**Unit:** records

#### BOM and routing lines

**Key:** bom

**Type:** number

**Default:** 40000

**Min:** 0

**Step:** 1000

**Unit:** lines

#### Load throughput per stream

**Key:** rate

**Type:** number

**Default:** 2500

**Min:** 1

**Step:** 100

**Unit:** records per hour

**Help:** Measure it in a test load; API or import-scenario throughput varies with validation, workflows and customisations.

#### Parallel load streams

**Key:** streams

**Type:** slider

**Default:** 3

**Min:** 1

**Max:** 10

**Step:** 1

**Unit:** streams

#### Rejected records on first pass

**Key:** errors

**Type:** slider

**Default:** 4

**Min:** 0

**Max:** 30

**Step:** 1

**Unit:** %

#### Cutover window available

**Key:** window

**Type:** number

**Default:** 36

**Min:** 1

**Step:** 1

**Unit:** hours

#### Validation and sign-off time

**Key:** validation

**Type:** number

**Default:** 8

**Min:** 0

**Step:** 1

**Unit:** hours

**Outputs:**

#### Total records

**Key:** total

**Expr:** master + txn + bom

**Format:** integer

#### First pass load time

**Key:** first

**Expr:** total / (rate * streams)

**Format:** hours

#### Rework load time

**Key:** rework

**Expr:** total * errors / 100 / (rate * streams) * 1.5

**Format:** hours

**Description:** Rejected records are reloaded with a 1.5x factor for investigation and correction overhead.

#### Total elapsed time with validation

**Key:** elapsed

**Expr:** first + rework + validation

**Format:** hours

**Primary:** true

#### Slack in the window (negative means overrun)

**Key:** slack

**Expr:** window - elapsed

**Format:** hours

**Primary:** true

**Chart:**

#### Elapsed hours by number of parallel streams

**Type:** line

**X:** streams

**From:** 1

**To:** 10

**Steps:** 10

**Series:**

- elapsed

**Disclaimer:** Throughput is the key input and must come from a rehearsal load in a copy of your own environment.

## Content

**Intro:** Cutover weekends fail when nobody timed the load. This calculator turns row counts and a measured throughput into elapsed hours, including the reloading of rejected records and your validation time, and tells you whether the window is big enough.

**Sections:**

#### How to use it

**Body:** Count master records, open transactions and BOM and routing lines in the extract. Take throughput from a rehearsal load, not from a guess, because validations, workflows and custom code change it dramatically. Add the number of parallel streams you can run without lock contention, the expected error rate and your validation time. Compare the elapsed total with the window you can negotiate with the business.

#### The model

**Body:** First pass time is total records divided by throughput x streams. Rework adds the rejected share loaded again at 1.5 times cost, and validation is added as a flat period. The chart shows diminishing returns as streams increase; in practice dependencies such as items before BOMs limit parallelism, and the database will eventually saturate.

**Bullets:**

- Load in dependency order: master data, then open items.
- Run at least two full rehearsals and update the throughput.
- Reduce volume by archiving closed history instead of migrating it.

#### Common mistakes

**Body:** Teams assume the rehearsal error rate will hold on production data, forget cutover-day freeze and extract time, and schedule no buffer. Keep a rollback decision point in the runbook. If the model shows negative slack, cut scope or add rehearsal-proven streams, not hope.

**Cta:** Netray can help profile source data and script the loads so the weekend timing is known before it matters.

**Faq:**

#### Where do I get throughput?

**Answer:** From a rehearsal load in a sandbox with production-like data and customisations. Vendor numbers on empty systems will be far higher than your real figure.

#### How many streams is safe?

**Answer:** It depends on dependencies and database contention. Start with two or three, measure, and increase only while throughput per stream stays steady.

#### Should I migrate history?

**Answer:** Usually only open items and balances. Closed history can be archived or summarised, which shortens the load and reduces errors.

#### What if slack is negative?

**Answer:** Reduce records, increase proven parallelism, widen the window or split the cutover into phases. Do not plan to compress validation.

## Search terms

acumatica data migration time, erp data load time estimate, cutover weekend planning, import scenario throughput, sage to acumatica data migration, go live data load window

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